In recent years, there has been rapid development of Internet technologies, and various products based on Internet technologies have been developed. To better maintain and optimize these products and to provide a better user experience, user behavior data of these products needs to be collected. User behavior data includes reasons for uninstalling a product, the number of successful installations of the product, defects encountered when using the product, the phase in which the defects occur, time spent on pages, the phase in which users exit the product, the defect rate of the product, reasons for crashes of the product, and so on. In this way, market penetration of the product and the product's number active users is known. At the same time, the product's quality is tracked. In addition, user habits, causes for defects or crashes, and influences of the product are also learned.
In the prior art, user behavior data as to a product is typically obtained by conducting questionnaires or surveys of users at critical times (e.g., when the user installs or uninstalls the product), and the information from the questionnaires or surveys is manually analyzed.
Such a method for collecting user behavior data largely relies on user input; therefore, the user behavior data is subjective and uncertain. Not only is the data collection range limited, but, also, the reliability of the collected user behavior data is poor. This greatly affects the accuracy of results obtained from analyzing the user behavior data.